6 papers
FluxFlow: Conservative Flow-Matching for Astronomical Image Super-Resolution
Shuhong Liu, Xining Ge, Ziteng Cui +8
Ground-to-space astronomical super-resolution requires recovering space-quality images from ground-based observations that are simultaneously limited by pixel sampling resolution a…
Training-Free Model Ensemble for Single-Image Super-Resolution via Strong-Branch Compensation
Gengjia Chang, Xining Ge, Weijun Yuan +4
Single-image super-resolution has progressed from deep convolutional baselines to stronger Transformer and state-space architectures, yet the corresponding performance gains typica…
Beyond Model Design: Data-Centric Training and Self-Ensemble for Gaussian Color Image Denoising
Gengjia Chang, Xining Ge, Weijun Yuan +4
This paper presents our solution to the NTIRE 2026 Image Denoising Challenge (Gaussian color image denoising at fixed noise level ). Rather than proposing a new restoration…
Dual-Branch Remote Sensing Infrared Image Super-Resolution
Xining Ge, Gengjia Chang, Weijun Yuan +6
Remote sensing infrared image super-resolution aims to recover sharper thermal observations from low-resolution inputs while preserving target contours, scene layout, and radiometr…
CLIP-Guided Data Augmentation for Night-Time Image Dehazing
Xining Ge, Weijun Yuan, Gengjia Chang +2
Nighttime image dehazing faces a more complex degradation pattern than its daytime counterpart, as haze scattering couples with low illumination, non-uniform lighting, and strong l…
Denoising the Deep Sky: Physics-Based CCD Noise Formation for Astronomical Imaging
Shuhong Liu, Xining Ge, Ziying Gu +7
Astronomical imaging remains noise-limited under practical observing conditions. Standard calibration pipelines remove structured artifacts but largely leave stochastic noise unres…